AI in Veterinary Radiology: What to Know

Artificial Intelligence (AI) is a powerful assistive tool in veterinary medicine. In the world of imaging, it offers exciting possibilities for new approaches to current workflows that can impact patient outcomes, support starting treatment plans sooner, and in the best-case scenario, relieve or support decision fatigue associated with patient care.

While AI’s potential is clear, it is important to recognize that its implementation may call for a shift in how veterinarians approach imaging, read radiographs, and initiate their diagnostic pathways. This article explores why AI is important, how to use it effectively, and the collaborative effort needed to integrate this technology into practice.

The gains associated with fast, consistent patient screening results are at the core of its unique value. By analyzing images for specific patterns and abnormalities, an AI report can highlight areas of concern and support veterinarians in making confident treatment decisions. By all means, rely on your expertise in reading radiographs, but why not check your answers when you can?

Asking for help is a critical skill in a successful practice. Vetology’s Virtual AI Radiology Report is just that: a support tool, an answer sheet, a guide. It’s one of many tools in your medical toolkit, and it is essential to remember that it was never intended to replace veterinary expertise nor radiologists; it was built to complement both.

In practice, AI tools analyze radiographs by running specialized classifiers tailored to detect specific conditions or abnormalities. For example, when assessing a feline abdomen radiograph, the AI might evaluate features like liver size or the presence of urocystoliths. These observations are presented as screening results, not diagnoses, guiding veterinarians toward further tests or treatments.

The effectiveness of AI depends on the quality of the submitted radiographs. Clear, well-positioned lateral and VD images that focus on the area of concern lead to more accurate reports. This underscores the importance of maintaining high imaging standards in clinical workflows.

As with any new tool, skillset, or appliance, adopting AI in veterinary medicine involves a learning curve, some change, and maybe some practice. AI is evolving and improving.  Developing effective tools requires close collaboration between veterinary professionals and developers. Input from veterinarians helps refine systems, ensuring they address real-world clinical needs. Academic peer-reviews support the integrity of the tool, and clinicians benefit from training, practice, and patience with these tools, understanding their capabilities and limitations.

Vetology views integrating with veterinary workflows as a collective effort. Our collective success depends on thoughtful implementation, high-quality radiographs, and collaboration. By working together, veterinarians, radiologists, and technologists can create tools that reinvent workflows that support patient care and maintain the highest standards of safety. This partnership is critical to ensuring that this new approach to imaging evolves as a trusted and valuable resource for the veterinary community.